Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

Abstract

Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications. INDEX TERMS Deep neural networks, OFDM systems, channel estimation, underwater acoustic communication.

Pilot Sequence Assisted Channel Estimation in MIMO/SIMO/SISO-OFDM Systems

“…In the future, we will apply the proposed pilot sequence assisted channel estimation method in cognitive MIMO-OFDM system [31]- [33], massive SIMO-OFDM systems, UWB MIMO-OFDM systems [34], CMMB MIMO-OFDM systems [35], massive MIMO-OFDM systems [36]- [40] and underwater acoustic OFDM systems [41].…”

Adaptive Elastic Echo State Network for Channel Prediction in IEEE802.11ah Standard-Based OFDM System

“…Then, we briefly explain the basic channel estimation in OFDM system, which is LS. Another channel estimation method, MMSE, is also optional and its theories are found in [28]. We further assume K subcarriers in one OFDM symbol and omit cyclic-prefix (CP) and series-toparallel (SP) processes, the k-th complex baseband timedomain sending signal s k (n) at the sample point n is…”

Design of Communication Systems Using Deep Learning: A Variational Inference Perspective

“…In molecular communication systems, a deep learning based approach to optimize the receiver design in the presence of Inter-Symbol Interference (ISI) is presented in [12]. In the context of Underwater Acoustic communications, [13] proposes a novel channel estimation technique for Orthogonal Frequency Division Multiplexing (OFDM) systems which is capable of providing better performance than traditional Least Squares (LS) and Minimum Mean Square Error (MMSE) estimators.…”